Foody -智能餐厅管理和点菜系统

V. Liyanage, A. Ekanayake, H.D.S.N Premasiri, P. Munasinghe, S. Thelijjagoda
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引用次数: 10

摘要

顾客在当代食品工业中扮演着至关重要的角色,决定着餐厅和食物的质量。餐馆非常重视顾客对其服务的反馈,因为生意的声誉取决于此。评估客户满意度的关键因素是,能否有效地提供服务,以减少消费时间,以及保持高质量的服务。在大多数情况下,选择一家著名的餐厅,顾客关注的是他们最喜欢的食物的选择,除了可用的座位和空间的选择。长时间的等待和上菜错误是每个餐馆都会发生的常见错误,最终导致顾客不满。这个在线应用程序“Foody”的目标是解决这些不足,并通过为每个客户提供独特的菜单来考虑他们的口味,为客户提供高效和准确的服务。这个概念被实现为一个移动应用程序,使用最新的IT概念,如商业智能,数据挖掘,预测分析和人工智能。这包括图形和3D建模,提供与食物相关的现有物理信息,如颜色、大小,用户可以进一步查看食物的成分以及可用的桌子。此外,该应用程序还可以显示餐厅的实时地图。当前的表保留状态由表的颜色变化表示。通过分析每个顾客的社交媒体信息,系统会为他们提供独特的食物推荐和订单,并通过计算等待时间来通知顾客。食物的准备和分配是主观的。该研究的预期结果是开发一个具有上述功能的全自动餐厅管理系统,并避免订单之间的混淆,提供更好的食物视图,并允许客户在最短的时间内根据自己的口味选择菜单。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Foody - Smart Restaurant Management and Ordering System
Customers play a vital role in the contemporary food industry when determining the quality of the restaurant and its food. Restaurants give considerable attention to customers’ feedback about their service, since the reputation of the business depends on it. Key factors of evaluating customer satisfaction are, being able to deliver the services effectively to lessen the time of consumption, as well as maintaining a high quality of service. In most cases of selecting a prominent restaurant, customers focus on their choice of favorite food in addition to available seating and space options. Long waiting times and serving the wrong order is a common mistake that happens in every restaurant that eventually leads to customer dissatisfaction. Objectives of this online application “Foody” is to address these deficiencies and provide efficient and accurate services to the customer, by providing unique menus to each customer considering their taste. This concept is implemented as a mobile application using latest IT concepts such as Business Intelligence, Data Mining, Predictive Analysis and Artificial Intelligence. This includes graphics and 3D modeling that provide existent physical information related to food such as colors, sizes and further user can view the ingredients of the meal as well as the available tables. In addition, the app shows the real-time map to the restaurant. Current table reservation status is indicated by the color change of the table. Unique food recommendation and it’s order for each customer is generated by analyzing their social media information and the system notifies the customer the wait time by calculating it. Preparation of food and allocation is done subjectively. The expected outcome of the research is to develop a fully automated restaurant management system with the mentioned features as well as to avoid confusions between orders, provide better view of food and allow the customer to choose the menu according to their taste in a minimum time.
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